import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.font_manager import FontProperties
from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier
from sklearn import metrics
from sklearn.model_selection import GridSearchCV

# font = FontProperties(fname='/Library/Fonts/Heiti.ttc')
def plot_decision_regions(X, y, classifier=None):
    marker_list = ['o', 'x', 's']
    color_list = ['r', 'b', 'g']
    cmap = ListedColormap(color_list[:len(np.unique(y))])

    x1_min, x1_max = X[:, 0].min()-1, X[:, 0].max()+1
    x2_min, x2_max = X[:, 1].min()-1, X[:, 1].max()+1
    t1 = np.linspace(x1_min, x1_max, 666)
    t2 = np.linspace(x2_min, x2_max, 666)

    x1, x2 = np.meshgrid(t1, t2)
    y_hat = classifier.predict(np.array([x1.ravel(), x2.ravel()]).T)
    y_hat = y_hat.reshape(x1.shape)
    plt.contourf(x1, x2, y_hat, alpha=0.2, cmap=cmap)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)

    print(marker_list)
    for ind, clas in enumerate(np.unique(y)) :
        print(ind,clas)
        plt.scatter(X[y == clas, 0], X[y == clas, 1], alpha=0.8, s=50,
                    c=color_list[clas], marker=marker_list[ind], label=label_list[1])

if __name__ == '__main__':
    iris_data = load_iris()
    X = iris_data.data[0:-1, [2, 3]]
    y = iris_data.target[0:-1]
    label_list = ['山鸢尾', '杂色鸢尾']

    #训练模型
    gbc = GradientBoostingClassifier(random_state=1)
    gbc.fit(X, y)
    y_pred = gbc.predict(X)
    y_predprob = gbc.predict_proba(X)[:, 1]
    print("精准度:{:.4f}".format(metrics.accuracy_score(y, y_pred)))
    # print("AUC分数(训练集):{:.4f}".format(metrics.roc_auc_score(y, y_predprob)))

    plot_decision_regions(X, y, classifier=gbc)
    # plt.xlabel('花瓣长度（cm）', fontproperties=font)
    # plt.ylabel('花瓣宽度（cm）', fontproperties=font)
    # plt.title('梯度提升法算法代码(鸢尾花分类)',fontproperties=font, fontsize=20)
    # plt.legend(prop=font)
    plt.show()
